Python实现带GUI界面的手写数字识别代码

本篇文章小编给大家分享一下Python实现带GUI界面的手写数字识别代码，文章代码介绍的很详细，小编觉得挺不错的，现在分享给大家供大家参考，有需要的小伙伴们可以来看看。

效果图

有点low，轻喷

点击选择图片会优先从当前目录查找

数据集

这部分我是对MNIST数据集进行处理保存

对应代码：

import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
from PIL import Image
import numpy as np
from scipy import misc
(x_train_all,y_train_all),(x_test,y_test) = tf.keras.datasets.mnist.load_data()
x_valid,x_train = x_train_all[:5000],x_train_all[5000:]
y_valid,y_train = y_train_all[:5000],y_train_all[5000:]
print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)
#读取单张图片
def show_single_img(img_arr,len=100,path='/Users/zhangcaihui/Desktop/case/jpg/'):
    for i in range(len):#我这种写法会进行覆盖，只能保存10张照片，想保存更多的数据自己看着改
        new_im = Image.fromarray(img_arr[i])  # 调用Image库，数组归一化
        #new_im.show()
        #plt.imshow(img_arr)  # 显示新图片
        label=y_train[i]
        new_im.save(path+str(label)+'.jpg')  # 保存图片到本地

#显示多张图片
def show_imgs(n_rows,n_cols,x_data,y_data):
    assert len(x_data) == len(y_data)
    assert n_rows * n_cols < len(x_data)
    plt.figure(figsize=(n_cols*1.4,n_rows*1.6))
    for row in range(n_rows):
        for col in range(n_cols):
            index = n_cols * row + col
            plt.subplot(n_rows,n_cols,index+1)
            plt.imshow(x_data[index],cmap="binary",interpolation="nearest")
            plt.axis("off")
    plt.show()
#show_imgs(2,2,x_train,y_train)
show_single_img(x_train)

关于GUI设计

1）排版

#ui_openimage.py
# -*- coding: utf-8 -*-
# from PyQt5 import QtCore, QtGui, QtWidgets
# from PyQt5.QtCore import Qt
import sys,time
from PyQt5 import QtGui, QtCore, QtWidgets
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *

class Ui_Form(object):
    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(1144, 750)
        self.label_1 = QtWidgets.QLabel(Form)
        self.label_1.setGeometry(QtCore.QRect(170, 130, 351, 251))
        self.label_1.setObjectName("label_1")
        self.label_2 = QtWidgets.QLabel(Form)
        self.label_2.setGeometry(QtCore.QRect(680, 140, 351, 251))
        self.label_2.setObjectName("label_2")
        self.btn_image = QtWidgets.QPushButton(Form)
        self.btn_image.setGeometry(QtCore.QRect(270, 560, 93, 28))
        self.btn_image.setObjectName("btn_image")
        self.btn_recognition = QtWidgets.QPushButton(Form)
        self.btn_recognition.setGeometry(QtCore.QRect(680,560,93,28))
        self.btn_recognition.setObjectName("bnt_recognition")
        #显示时间按钮
        self.bnt_timeshow = QtWidgets.QPushButton(Form)
        self.bnt_timeshow.setGeometry(QtCore.QRect(900,0,200,50))
        self.bnt_timeshow.setObjectName("bnt_timeshow")
        self.retranslateUi(Form)
        self.btn_image.clicked.connect(self.slot_open_image)
        self.btn_recognition.clicked.connect(self.slot_output_digital)
        self.bnt_timeshow.clicked.connect(self.buttonClicked)
        self.center()
        QtCore.QMetaObject.connectSlotsByName(Form)

    def retranslateUi(self, Form): #设置文本填充label、button
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "数字识别系统"))
        self.label_1.setText(_translate("Form", "点击下方按钮"))
        self.label_1.setStyleSheet('font:50px;')
        self.label_2.setText(_translate("Form", "0~9"))
        self.label_2.setStyleSheet('font:50px;')
        self.btn_image.setText(_translate("Form", "选择图片"))
        self.btn_recognition.setText(_translate("From","识别结果"))
        self.bnt_timeshow.setText(_translate("Form","当前时间"))

    # 状态条显示时间模块
    def buttonClicked(self):  # 动态显示时间
        timer = QTimer(self)
        timer.timeout.connect(self.showtime)
        timer.start()
    def showtime(self):
        datetime = QDateTime.currentDateTime()
        time_now = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())
        #self.statusBar().showMessage(time_now)
        #self.bnt_timeshow.setFont(QtGui.QFont().setPointSize(100))
        self.bnt_timeshow.setText(time_now)

    def center(self):#窗口放置中央
        screen = QDesktopWidget().screenGeometry()
        size = self.geometry()
        self.move((screen.width() - size.width()) / 2,
                    (screen.height() - size.height()) / 2)


    def keyPressEvent(self, e):
        if e.key() == Qt.Key_Escape:
            self.close()

2）直接运行这个文件（调用1）

#ui_main.py
import random

from PyQt5.QtWidgets import QFileDialog
from PyQt5.QtGui import QPixmap
from ui_openimage import Ui_Form
import sys
from PyQt5 import QtWidgets, QtGui
from PyQt5.QtWidgets import QMainWindow, QTextEdit, QAction, QApplication
import os,sys
from PyQt5.QtCore import Qt

import tensorflow
from tensorflow.keras.models import load_model
from tensorflow.keras.datasets import mnist
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
import tensorflow.keras.preprocessing.image as image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import warnings
warnings.filterwarnings("ignore")
class window(QtWidgets.QMainWindow,Ui_Form):
    def __init__(self):
        super(window, self).__init__()
        self.cwd = os.getcwd()
        self.setupUi(self)
        self.labels = self.label_1
        self.img=None
    def slot_open_image(self):
        file, filetype = QFileDialog.getOpenFileName(self, '打开多个图片', self.cwd, "*.jpg, *.png, *.JPG, *.JPEG, All Files(*)")
        jpg = QtGui.QPixmap(file).scaled(self.labels.width(), self.labels.height())
        self.labels.setPixmap(jpg)
        self.img=file

    def slot_output_digital(self):
    	'''path为之前保存的模型路径'''
        path='/Users/zhangcaihui/PycharmProjects/py38_tf/DL_book_keras/save_the_model.h5'
        model= load_model(path)
        #防止不上传数字照片而直接点击识别
        if self.img==None:
            self.label_2.setText('请上传照片！')
            return
        img = image.load_img(self.img, target_size=(28, 28))
        img = img.convert('L')#转灰度图像
        x = image.img_to_array(img)
        #x = abs(255 - x)
        x = np.expand_dims(x, axis=0)
        print(x.shape)
        x = x / 255.0
        prediction = model.predict(x)
        print(prediction)
        output = np.argmax(prediction, axis=1)
        print("手写数字识别为：" + str(output[0]))
        self.label_2.setText(str(output[0]))

if __name__ == "__main__":
  app = QtWidgets.QApplication(sys.argv)
  my = window()
  my.show()
  sys.exit(app.exec_())

缺点

界面low

只能识别单个数字

其实可以将多数字图片进行裁剪分割，这就涉及到制作数据集了